Overview

Dataset statistics

Number of variables11
Number of observations4312
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory404.2 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

GrossRevenue is highly overall correlated with QtItems and 4 other fieldsHigh correlation
RecencyDays is highly overall correlated with PurchasesNo and 2 other fieldsHigh correlation
Frequency is highly overall correlated with PurchasesNo and 2 other fieldsHigh correlation
ReturnsNo is highly overall correlated with ProductReturns and 3 other fieldsHigh correlation
QtItems is highly overall correlated with GrossRevenue and 4 other fieldsHigh correlation
ProductReturns is highly overall correlated with ReturnsNo and 1 other fieldsHigh correlation
PurchasesNo is highly overall correlated with GrossRevenue and 7 other fieldsHigh correlation
PurchasingDays is highly overall correlated with GrossRevenue and 7 other fieldsHigh correlation
ProductsNo is highly overall correlated with GrossRevenue and 4 other fieldsHigh correlation
PurchasingMonths is highly overall correlated with GrossRevenue and 8 other fieldsHigh correlation
GrossRevenue is highly skewed (γ1 = 21.48774973)Skewed
QtItems is highly skewed (γ1 = 22.85121717)Skewed
ProductReturns is highly skewed (γ1 = 26.98231359)Skewed
CustomerID has unique valuesUnique
RecencyDays has 103 (2.4%) zerosZeros
ReturnsNo has 2823 (65.5%) zerosZeros
ProductReturns has 2823 (65.5%) zerosZeros

Reproduction

Analysis started2023-10-15 23:00:50.954531
Analysis finished2023-10-15 23:01:11.149171
Duration20.19 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

Distinct4312
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.228
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:11.412590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12618.55
Q113815.75
median15299.5
Q316780.25
95-th percentile17981.8
Maximum18287
Range5940
Interquartile range (IQR)2964.5

Descriptive statistics

Standard deviation1720.3287
Coefficient of variation (CV)0.11243076
Kurtosis-1.1951977
Mean15301.228
Median Absolute Deviation (MAD)1482.5
Skewness0.0019242029
Sum65978894
Variance2959530.9
MonotonicityNot monotonic
2023-10-15T20:01:11.580589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
15700 1
 
< 0.1%
15219 1
 
< 0.1%
15014 1
 
< 0.1%
14765 1
 
< 0.1%
16869 1
 
< 0.1%
15909 1
 
< 0.1%
13618 1
 
< 0.1%
16050 1
 
< 0.1%
17879 1
 
< 0.1%
Other values (4302) 4302
99.8%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
12357 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18281 1
< 0.1%
18280 1
< 0.1%
18278 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%

GrossRevenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4234
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1923.3688
Minimum12.24
Maximum278778.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:11.762593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.24
5-th percentile110.9365
Q1300.9025
median656
Q31612.295
95-th percentile5660.269
Maximum278778.02
Range278765.78
Interquartile range (IQR)1311.3925

Descriptive statistics

Standard deviation8328.4019
Coefficient of variation (CV)4.3301118
Kurtosis594.65421
Mean1923.3688
Median Absolute Deviation (MAD)454.37
Skewness21.48775
Sum8293566.1
Variance69362278
MonotonicityNot monotonic
2023-10-15T20:01:11.914589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.32 4
 
0.1%
35.4 3
 
0.1%
15 3
 
0.1%
363.65 3
 
0.1%
440 3
 
0.1%
79.2 3
 
0.1%
113.5 3
 
0.1%
181.09 2
 
< 0.1%
324.24 2
 
< 0.1%
590 2
 
< 0.1%
Other values (4224) 4284
99.4%
ValueCountFrequency (%)
12.24 1
 
< 0.1%
12.75 1
 
< 0.1%
15 3
0.1%
17 1
 
< 0.1%
20.8 2
< 0.1%
25.5 1
 
< 0.1%
30 1
 
< 0.1%
30.6 1
 
< 0.1%
32.65 1
 
< 0.1%
34 1
 
< 0.1%
ValueCountFrequency (%)
278778.02 1
< 0.1%
259657.3 1
< 0.1%
189735.53 1
< 0.1%
132995.13 1
< 0.1%
123638.18 1
< 0.1%
114501.32 1
< 0.1%
88138.2 1
< 0.1%
65920.12 1
< 0.1%
62904.1 1
< 0.1%
59419.34 1
< 0.1%

RecencyDays
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct349
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.626391
Minimum0
Maximum373
Zeros103
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:12.087589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median49
Q3138
95-th percentile310
Maximum373
Range373
Interquartile range (IQR)122

Descriptive statistics

Standard deviation99.435154
Coefficient of variation (CV)1.1094406
Kurtosis0.48470823
Mean89.626391
Median Absolute Deviation (MAD)40
Skewness1.2644794
Sum386469
Variance9887.3498
MonotonicityNot monotonic
2023-10-15T20:01:12.268589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 126
 
2.9%
8 104
 
2.4%
0 103
 
2.4%
2 99
 
2.3%
1 91
 
2.1%
7 78
 
1.8%
9 73
 
1.7%
10 70
 
1.6%
21 62
 
1.4%
17 61
 
1.4%
Other values (339) 3445
79.9%
ValueCountFrequency (%)
0 103
2.4%
1 91
2.1%
2 99
2.3%
3 126
2.9%
4 60
1.4%
5 22
 
0.5%
6 42
 
1.0%
7 78
1.8%
8 104
2.4%
9 73
1.7%
ValueCountFrequency (%)
373 7
0.2%
372 16
0.4%
371 10
0.2%
370 2
 
< 0.1%
369 1
 
< 0.1%
368 5
 
0.1%
367 2
 
< 0.1%
366 7
0.2%
365 10
0.2%
364 9
0.2%

Frequency
Real number (ℝ)

Distinct1335
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37891325
Minimum0.0054644809
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:12.449589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054644809
5-th percentile0.010928962
Q10.022701563
median0.051282051
Q31
95-th percentile1
Maximum6
Range5.9945355
Interquartile range (IQR)0.97729844

Descriptive statistics

Standard deviation0.5091226
Coefficient of variation (CV)1.3436389
Kurtosis6.4701184
Mean0.37891325
Median Absolute Deviation (MAD)0.037897024
Skewness1.6615113
Sum1633.8739
Variance0.25920582
MonotonicityNot monotonic
2023-10-15T20:01:12.618593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1309
30.4%
2 60
 
1.4%
0.1428571429 22
 
0.5%
0.4 21
 
0.5%
0.02777777778 17
 
0.4%
0.3333333333 17
 
0.4%
0.1818181818 17
 
0.4%
0.09090909091 17
 
0.4%
0.25 16
 
0.4%
0.07142857143 14
 
0.3%
Other values (1325) 2802
65.0%
ValueCountFrequency (%)
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005524861878 1
 
< 0.1%
0.005602240896 3
0.1%
0.005633802817 2
< 0.1%
0.005681818182 2
< 0.1%
0.005698005698 2
< 0.1%
0.005714285714 3
0.1%
0.005730659026 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 6
 
0.1%
2 60
 
1.4%
1.833333333 1
 
< 0.1%
1.5 3
 
0.1%
1.333333333 1
 
< 0.1%
1 1309
30.4%
0.8333333333 1
 
< 0.1%

ReturnsNo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77272727
Minimum0
Maximum45
Zeros2823
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:12.777589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.975216
Coefficient of variation (CV)2.5561619
Kurtosis151.18414
Mean0.77272727
Median Absolute Deviation (MAD)0
Skewness9.1673092
Sum3332
Variance3.9014782
MonotonicityNot monotonic
2023-10-15T20:01:13.147591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2823
65.5%
1 836
 
19.4%
2 288
 
6.7%
3 139
 
3.2%
4 92
 
2.1%
5 37
 
0.9%
6 32
 
0.7%
7 21
 
0.5%
9 8
 
0.2%
11 5
 
0.1%
Other values (13) 31
 
0.7%
ValueCountFrequency (%)
0 2823
65.5%
1 836
 
19.4%
2 288
 
6.7%
3 139
 
3.2%
4 92
 
2.1%
5 37
 
0.9%
6 32
 
0.7%
7 21
 
0.5%
8 5
 
0.1%
9 8
 
0.2%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

QtItems
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1766
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1133.6389
Minimum1
Maximum196556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:13.310589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1159
median373.5
Q3978
95-th percentile3504.6
Maximum196556
Range196555
Interquartile range (IQR)819

Descriptive statistics

Standard deviation4696.3904
Coefficient of variation (CV)4.1427569
Kurtosis772.70619
Mean1133.6389
Median Absolute Deviation (MAD)272.5
Skewness22.851217
Sum4888251
Variance22056083
MonotonicityNot monotonic
2023-10-15T20:01:13.495591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 19
 
0.4%
120 18
 
0.4%
72 16
 
0.4%
128 15
 
0.3%
106 15
 
0.3%
78 14
 
0.3%
146 14
 
0.3%
150 14
 
0.3%
84 13
 
0.3%
160 13
 
0.3%
Other values (1756) 4161
96.5%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 4
0.1%
3 2
 
< 0.1%
4 7
0.2%
5 3
0.1%
6 3
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 5
0.1%
ValueCountFrequency (%)
196556 1
< 0.1%
76946 1
< 0.1%
76631 1
< 0.1%
69041 1
< 0.1%
64124 1
< 0.1%
63014 1
< 0.1%
61308 1
< 0.1%
57921 1
< 0.1%
56926 1
< 0.1%
49391 1
< 0.1%

ProductReturns
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct205
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.980056
Minimum0
Maximum9014
Zeros2823
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:13.686589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile57
Maximum9014
Range9014
Interquartile range (IQR)3

Descriptive statistics

Standard deviation233.47567
Coefficient of variation (CV)10.159926
Kurtosis890.17853
Mean22.980056
Median Absolute Deviation (MAD)0
Skewness26.982314
Sum99090
Variance54510.887
MonotonicityNot monotonic
2023-10-15T20:01:13.853589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2823
65.5%
1 169
 
3.9%
2 148
 
3.4%
3 105
 
2.4%
4 89
 
2.1%
6 78
 
1.8%
5 61
 
1.4%
12 51
 
1.2%
7 44
 
1.0%
8 43
 
1.0%
Other values (195) 701
 
16.3%
ValueCountFrequency (%)
0 2823
65.5%
1 169
 
3.9%
2 148
 
3.4%
3 105
 
2.4%
4 89
 
2.1%
5 61
 
1.4%
6 78
 
1.8%
7 44
 
1.0%
8 43
 
1.0%
9 41
 
1.0%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

PurchasesNo
Real number (ℝ)

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2608998
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:14.029589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.6592577
Coefficient of variation (CV)1.7975681
Kurtosis244.21556
Mean4.2608998
Median Absolute Deviation (MAD)1
Skewness11.953473
Sum18373
Variance58.664228
MonotonicityNot monotonic
2023-10-15T20:01:14.207589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1491
34.6%
2 825
19.1%
3 500
 
11.6%
4 395
 
9.2%
5 236
 
5.5%
6 173
 
4.0%
7 139
 
3.2%
8 98
 
2.3%
9 68
 
1.6%
10 55
 
1.3%
Other values (46) 332
 
7.7%
ValueCountFrequency (%)
1 1491
34.6%
2 825
19.1%
3 500
 
11.6%
4 395
 
9.2%
5 236
 
5.5%
6 173
 
4.0%
7 139
 
3.2%
8 98
 
2.3%
9 68
 
1.6%
10 55
 
1.3%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%

PurchasingDays
Real number (ℝ)

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.239564
Minimum1
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:14.381588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum207
Range206
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.59582
Coefficient of variation (CV)1.7916512
Kurtosis253.32318
Mean4.239564
Median Absolute Deviation (MAD)1
Skewness12.165239
Sum18281
Variance57.696481
MonotonicityNot monotonic
2023-10-15T20:01:14.559593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1493
34.6%
2 823
19.1%
3 500
 
11.6%
4 398
 
9.2%
5 237
 
5.5%
6 172
 
4.0%
7 137
 
3.2%
8 96
 
2.2%
9 76
 
1.8%
11 54
 
1.3%
Other values (46) 326
 
7.6%
ValueCountFrequency (%)
1 1493
34.6%
2 823
19.1%
3 500
 
11.6%
4 398
 
9.2%
5 237
 
5.5%
6 172
 
4.0%
7 137
 
3.2%
8 96
 
2.2%
9 76
 
1.8%
10 52
 
1.2%
ValueCountFrequency (%)
207 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
83 1
< 0.1%
72 1
< 0.1%
60 1
< 0.1%
58 1
< 0.1%

ProductsNo
Real number (ℝ)

Distinct468
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.809833
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:14.736589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median41
Q3100
95-th percentile315.45
Maximum7837
Range7836
Interquartile range (IQR)83

Descriptive statistics

Standard deviation228.95531
Coefficient of variation (CV)2.4937994
Kurtosis481.70849
Mean91.809833
Median Absolute Deviation (MAD)30
Skewness18.076734
Sum395884
Variance52420.533
MonotonicityNot monotonic
2023-10-15T20:01:14.919589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 84
 
1.9%
9 76
 
1.8%
6 76
 
1.8%
15 69
 
1.6%
11 68
 
1.6%
1 68
 
1.6%
28 67
 
1.6%
8 67
 
1.6%
5 66
 
1.5%
7 65
 
1.5%
Other values (458) 3606
83.6%
ValueCountFrequency (%)
1 68
1.6%
2 50
1.2%
3 55
1.3%
4 49
1.1%
5 66
1.5%
6 76
1.8%
7 65
1.5%
8 67
1.6%
9 76
1.8%
10 84
1.9%
ValueCountFrequency (%)
7837 1
< 0.1%
5670 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2697 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1672 1
< 0.1%
1636 1
< 0.1%

PurchasingMonths
Real number (ℝ)

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0839518
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-10-15T20:01:15.084593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.588611
Coefficient of variation (CV)0.83938115
Kurtosis2.0693461
Mean3.0839518
Median Absolute Deviation (MAD)1
Skewness1.5853553
Sum13298
Variance6.7009069
MonotonicityNot monotonic
2023-10-15T20:01:15.198588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1543
35.8%
2 897
20.8%
3 546
 
12.7%
4 398
 
9.2%
5 283
 
6.6%
6 175
 
4.1%
7 113
 
2.6%
8 99
 
2.3%
9 76
 
1.8%
10 64
 
1.5%
Other values (2) 118
 
2.7%
ValueCountFrequency (%)
1 1543
35.8%
2 897
20.8%
3 546
 
12.7%
4 398
 
9.2%
5 283
 
6.6%
6 175
 
4.1%
7 113
 
2.6%
8 99
 
2.3%
9 76
 
1.8%
10 64
 
1.5%
ValueCountFrequency (%)
12 61
 
1.4%
11 57
 
1.3%
10 64
 
1.5%
9 76
 
1.8%
8 99
 
2.3%
7 113
 
2.6%
6 175
 
4.1%
5 283
6.6%
4 398
9.2%
3 546
12.7%

Interactions

2023-10-15T20:01:08.824561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:52.353773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.348556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.989554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.549554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.076554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:00.727560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:02.469554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.995556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.487562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.229566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:08.960558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:52.812554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.477554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.123559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.705562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.203556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:00.876562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:02.605561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.121555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.632557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.375559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.096555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:52.965559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.610561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.264556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.854562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.335559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.012554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:02.736554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.251554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.773562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.518554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.230554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:53.128561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.748556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.411554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.994555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.476561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.172561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:02.886554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.386556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.922559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.678554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.367557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:53.285559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.880564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.554557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.131556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.605561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.327561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.028556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.521555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:06.252554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.826554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.496555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:53.433554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.011554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.691564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.258561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.735554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.474562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.152555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.666559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:06.376561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.964555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.641559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:53.603558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.157565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.840562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.407558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:59.877560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.665560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.295559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.819558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:06.521555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:08.121555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.771558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:53.747553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.293563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:56.977556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.536559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:00.002555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.829554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.439556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:04.944555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:06.653557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:08.256555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:09.900558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:53.894556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.430556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.109558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.664557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:00.133554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:01.997553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.586555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.080555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:06.797554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:08.397553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:10.043554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.041555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.570554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.243557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.794555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:00.425558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:02.145559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.725558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.216554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:06.942559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:08.531555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:10.189559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:54.210561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:55.726559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:57.391564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:00:58.943558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:00.581557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:02.316559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:03.864556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:05.359557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:07.092558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-10-15T20:01:08.686555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-10-15T20:01:15.340588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
CustomerIDGrossRevenueRecencyDaysFrequencyReturnsNoQtItemsProductReturnsPurchasesNoPurchasingDaysProductsNoPurchasingMonths
CustomerID1.000-0.0780.0160.023-0.045-0.068-0.0520.0000.001-0.004-0.028
GrossRevenue-0.0781.000-0.492-0.4430.4820.9310.4710.8060.8050.7870.790
RecencyDays0.016-0.4921.0000.338-0.308-0.491-0.296-0.561-0.560-0.499-0.547
Frequency0.023-0.4430.3381.000-0.186-0.410-0.188-0.581-0.581-0.392-0.641
ReturnsNo-0.0450.482-0.308-0.1861.0000.4500.9760.5020.5010.4110.559
QtItems-0.0680.931-0.491-0.4100.4501.0000.4430.7590.7580.7760.744
ProductReturns-0.0520.471-0.296-0.1880.9760.4431.0000.4850.4850.3960.539
PurchasesNo0.0000.806-0.561-0.5810.5020.7590.4851.0000.9990.7310.948
PurchasingDays0.0010.805-0.560-0.5810.5010.7580.4850.9991.0000.7310.948
ProductsNo-0.0040.787-0.499-0.3920.4110.7760.3960.7310.7311.0000.707
PurchasingMonths-0.0280.790-0.547-0.6410.5590.7440.5390.9480.9480.7071.000

Missing values

2023-10-15T20:01:10.426564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-15T20:01:10.666558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDGrossRevenueRecencyDaysFrequencyReturnsNoQtItemsProductReturnsPurchasesNoPurchasingDaysProductsNoPurchasingMonths
0178505288.633010.4861111.0169340.034.033.0297.02
1130473089.10450.0487807.0135535.09.08.0171.09
2125836629.3420.0458222.0497850.015.015.0232.011
313748948.25950.0179210.04390.05.04.028.03
415100635.103290.1363643.05822.03.03.03.02
5152914551.51250.0544415.0207329.014.014.0102.07
6146885107.3870.0735696.03222399.021.021.0327.011
7178095344.85150.0391062.0201641.012.012.061.08
81531159419.3400.31550827.037720474.091.091.02379.012
9160982005.63870.0278750.06130.07.08.067.07
CustomerIDGrossRevenueRecencyDaysFrequencyReturnsNoQtItemsProductReturnsPurchasesNoPurchasingDaysProductsNoPurchasingMonths
43471600012393.7023.00.051100.03.03.09.01
4348151953861.0021.00.014040.01.01.01.01
434914087181.6722.01.02501.01.01.069.01
435014204161.0311.00.0820.01.01.044.01
435115471469.4811.00.02660.01.01.077.01
435213436196.8911.00.0760.01.01.012.01
435315520343.5011.00.03140.01.01.018.01
435413298360.0001.00.0960.01.01.02.01
435514569227.3901.00.0790.01.01.012.01
435612713794.5501.00.05050.01.01.037.01